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https://github.com/amices/mice

Multivariate Imputation by Chained Equations
https://github.com/amices/mice

chained-equations fcs imputation mice missing-data missing-values multiple-imputation multivariate-data

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Multivariate Imputation by Chained Equations

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README 

        
---

output:

  md_document:

    variant: gfm

bibliography: refs.bibtex

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-"

)

options(width = 60, digits = 3)

set.seed(1)

```



# mice 



[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/mice)](https://cran.r-project.org/package=mice)

[![](https://cranlogs.r-pkg.org/badges/mice)](https://cran.r-project.org/package=mice)

[![R-CMD-check](https://github.com/amices/mice/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/amices/mice/actions/workflows/R-CMD-check.yaml)

[![](https://img.shields.io/badge/github%20version-3.16.16-orange.svg)](https://amices.org/mice/)



## [Multivariate Imputation by Chained Equations](https://amices.org/mice/)



The [`mice`](https://cran.r-project.org/package=mice) package

implements a method to deal with missing data. The package creates

multiple imputations (replacement values) for multivariate missing

data. The method is based on Fully Conditional Specification, where

each incomplete variable is imputed by a separate model. The `MICE`

algorithm can impute mixes of continuous, binary, unordered

categorical and ordered categorical data. In addition, MICE can impute

continuous two-level data, and maintain consistency between

imputations by means of passive imputation. Many diagnostic plots are

implemented to inspect the quality of the imputations.



## Installation



The `mice` package can be installed from CRAN as follows:



```{r eval = FALSE}

install.packages("mice")

```



The latest version can be installed from GitHub as follows: 



```{r eval = FALSE}

install.packages("devtools")

devtools::install_github(repo = "amices/mice")

```



## Minimal example



```{r pattern, fig.cap = "Missing data pattern of `nhanes` data. Blue is observed, red is missing."}

library(mice, warn.conflicts = FALSE)



# show the missing data pattern

md.pattern(nhanes)

```



The table and the graph summarize where the missing data occur in 

the `nhanes` dataset.



```{r stripplot, fig.cap = "Distribution of `chl` per imputed data set."}

# multiple impute the missing values

imp <- mice(nhanes, maxit = 2, m = 2, seed = 1)



# inspect quality of imputations

stripplot(imp, chl, pch = 19, xlab = "Imputation number")

```



In general, we would like the imputations to be plausible, i.e., 

values that could have been observed if they had not been missing.



```{r}

# fit complete-data model

fit <- with(imp, lm(chl ~ age + bmi))



# pool and summarize the results

summary(pool(fit))

```



The complete-data is fit to each imputed dataset, and the 

results are combined to arrive at estimates that properly 

account for the missing data.



## `mice 3.0`



Version 3.0 represents a major update that implements the 

following features: 



1. `blocks`: The main algorithm iterates over blocks. A block is

    simply a collection of variables. In the common MICE algorithm each 

    block was equivalent to one variable, which - of course - is 

    the default; The `blocks` argument allows mixing univariate 

    imputation method multivariate imputation methods. The `blocks` 

    feature bridges two seemingly disparate approaches, joint modeling 

    and fully conditional specification, into one framework;



2. `where`: The `where` argument is a logical matrix of the same size 

    of `data` that specifies which cells should be imputed. This opens 

    up some new analytic possibilities;

    

3.  Multivariate tests: There are new functions `D1()`, `D2()`, `D3()`

    and `anova()` that perform multivariate parameter tests on the 

    repeated analysis from on multiply-imputed data;



4. `formulas`: The old `form` argument has been redesign and is now 

    renamed to `formulas`. This provides an alternative way to specify

    imputation models that exploits the full power of R's native 

    formula's. 



5.  Better integration with the `tidyverse` framework, especially 

    for packages `dplyr`, `tibble` and  `broom`;

   

6.  Improved numerical algorithms for low-level imputation function. 

    Better handling of duplicate variables.



7.  Last but not least: A brand new edition AND online version of

    [Flexible Imputation of Missing Data. Second Edition.](https://stefvanbuuren.name/fimd/)



See [MICE: Multivariate Imputation by Chained Equations](https://amices.org/mice/) 

for more resources.



I'll be happy to take feedback and discuss suggestions. Please submit these 

through Github's issues facility.



## Resources



### Books



1. Van Buuren, S. (2018). [Flexible Imputation of Missing Data. Second Edition.](https://stefvanbuuren.name/fimd/). Chapman & Hall/CRC. Boca Raton, FL.



### Course materials



1. [Handling Missing Data in `R` with `mice`](https://amices.org/Winnipeg/)

2. [Statistical Methods for combined data sets](https://stefvanbuuren.name/RECAPworkshop/)



### Vignettes



1. [Ad hoc methods and the MICE algorithm](https://www.gerkovink.com/miceVignettes/Ad_hoc_and_mice/Ad_hoc_methods.html)

2. [Convergence and pooling](https://www.gerkovink.com/miceVignettes/Convergence_pooling/Convergence_and_pooling.html)

3. [Inspecting how the observed data and missingness are related](https://www.gerkovink.com/miceVignettes/Missingness_inspection/Missingness_inspection.html)

4. [Passive imputation and post-processing](https://www.gerkovink.com/miceVignettes/Passive_Post_processing/Passive_imputation_post_processing.html)

5. [Imputing multilevel data](https://www.gerkovink.com/miceVignettes/Multi_level/Multi_level_data.html)

6. [Sensitivity analysis with `mice`](https://www.gerkovink.com/miceVignettes/Sensitivity_analysis/Sensitivity_analysis.html)

7. [Generate missing values with `ampute`](https://rianneschouten.github.io/mice_ampute/vignette/ampute.html)

8. [`futuremice`: Wrapper for parallel MICE imputation through futures](https://www.gerkovink.com/miceVignettes/futuremice/Vignette_futuremice.html)



### Code from publications



1. [Flexible Imputation of Missing Data. Second edition.](https://github.com/stefvanbuuren/fimdbook/tree/master/R)



## Acknowledgement



The cute mice sticker was designed by Jaden M. Walters. Thanks Jaden!



## Code of Conduct



Please note that the mice project is released with a [Contributor Code of Conduct](https://amices.org/mice/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.